Robust IV inference with clustering dependence
针对线性工具变量模型中常见的聚类依赖性问题，提出一种对弱工具变量和聚类依赖均稳健的推断方法，基于Fama-MacBeth估计思想，通过截断无偏IV估计量来改善有限样本表现。
Summary Linear instrumental variables (IV) models with clustering dependence are widely used in empirical studies, although the common solution, the cluster covariance estimator, often produces undesirable inferential results, especially with weak instruments. In this paper, I propose a method that is robust to both weak IV and (potentially heterogeneous) clustering dependence. The proposed method is based on the idea of Fama–MacBeth estimation, with group-level estimators being a truncated version of the unbiased IV estimator. Truncation stabilizes the group-level estimator by ensuring bounded second moments, thus improving finite-sample performance in weak instrument settings. Asymptotic validity is shown under both strong and weak IV sequences, as well as under general requirements. The proposed method is applied to study the effect of city compactness on population density.